01 · Failure
Failure Prediction
Predict equipment or asset failure before downtime happens — by vibration, temperature, dwell, cycle count.
Forecast risk, failure, demand, congestion, delays and operational performance using real-time and historical data. Models retrain on operational ground truth — accuracy improves the moment your operation does.
Six stages, in a continuous cycle. Models improve every week because the loop is closed against real operational outcomes — not synthetic test sets.
Assets, sensors, RFID, workflows, inspections, ERP, WMS, EAM — fused into one feature surface.
Trends, recurring issues, delays, utilization patterns and operational behaviors.
Failures, demand, SLA breaches, congestion, shortages — with confidence intervals.
Compare every prediction to what actually happened — drift, accuracy, calibration tracked.
Weekly retrain against ground truth. Drift triggers earlier — models never go stale.
Surface the recommendation, the why, and the levers — feed it into agentic orchestration.
Predict equipment or asset failure before downtime happens — by vibration, temperature, dwell, cycle count.
Identify tasks and incidents likely to miss SLA — flagged with confidence + recommended escalation.
Inventory, asset, workforce, service demand — by site, shift, season, weather and event signals.
Congestion, delays, underutilized resources and workflow friction — spotted before they cascade.
Every asset, location, incident and workflow scored by probability × impact — the right thing first.
Better allocation of people, assets, equipment and capacity — recommendations with measurable lift.
Forecast equipment failure and pre-create the work order. Cut unplanned downtime 30–50% in field deployments.
Predict congestion in receiving, staging, picking and dispatch — re-route resources before bottlenecks form.
Forecast incident hotspots, traffic congestion, crowd density and response demand — staff to the load.
Predict temperature excursions before compliance is breached — alert the right team with time to act.
Predictive maintenance reduces unplanned outages across manufacturing and field assets.
Demand forecasting + replenishment AI lifts in-stock rates measurably.
SLA-pressure scoring + preemptive escalation cuts breach rates across service ops.
Forecast-driven crew, asset and capacity allocation improves utilization without staff growth.
A prediction is only useful if it arrives with enough lead time to act on. Every model is specified by what it forecasts, how far ahead it is trustworthy, and how often it is refreshed.
| Forecast | Useful horizon | Refresh | Precision | Acted on by |
|---|---|---|---|---|
| Equipment failure | 3–21 days | Hourly | 0.94 | Maintenance planning, parts pre-order |
| SLA breach risk | 1–8 hours | 5 min | 0.96 | Shift staffing, job resequencing |
| Demand and volume | 1–14 days | Hourly | 0.92 | Crew rosters, inbound scheduling |
| Congestion | 15–120 min | 1 min | 0.89 | Dock allocation, traffic diversion |
| Resource shortfall | 1–7 days | Daily | 0.91 | Overtime approval, contractor call-off |
| Energy and load | 1–48 hours | 15 min | 0.95 | Peak shaving, storage scheduling |
Every prediction ships with four things beyond the headline figure. Operators are trained to check all four before acting — and the interface refuses to show the number alone.
| Element | What it tells you | When to distrust it |
|---|---|---|
| Confidence interval | The range the true value is expected to fall in, widening with horizon. | Interval spans the decision threshold — the forecast cannot separate act from wait. |
| Driver attribution | Which inputs moved the number, ranked by contribution. | A single driver dominates above ~70% — usually a data problem, not a real signal. |
| Calibration state | Whether the model's stated 80% has actually been right 80% of the time. | Marked recalibrating — the probability is directionally useful but not numerically. |
| Support | How many comparable historical cases the forecast rests on. | Fewer than ~30 analogues, or none in this season or configuration. |
The hard part of prediction is not the first model — it is knowing, months later, whether it is still right. Every forecast is reconciled against the outcome automatically.
| Mechanism | How it works |
|---|---|
| Automatic reconciliation | When the horizon elapses, the prediction is compared to the recorded outcome from the operational ledger. No one has to file a report for this to happen. |
| Calibration curves | Predicted probability is plotted against observed frequency per model, per month. A model claiming 90% that lands at 70% is recalibrated before it is retrained. |
| Drift detection | Input distributions and output confidence are monitored continuously. Drift beyond threshold raises an incident against the model like any other asset fault. |
| The intervention problem | A forecast acted on successfully never comes true, which naively looks like a wrong prediction. Acted-on cases are tracked separately so intervention does not poison the accuracy record. |
| Champion / challenger | A candidate model runs on live traffic alongside production. Promotion requires winning on precision and on the cost of its false positives. |
| Retraining cadence | Weekly by default, immediately on drift breach. Every retrain is a versioned artefact with the data window recorded. |
Most disappointment with predictive analytics comes from expectations nobody stated at the outset. These are ours, stated at the outset.
| Limit | Why |
|---|---|
| It needs history | A new site or asset class has no analogues. Expect 8–12 weeks of baseline before failure prediction is trustworthy; SLA and demand models mature faster. |
| It cannot forecast the unprecedented | Models extrapolate from patterns. A genuinely novel failure mode or a step change in operations will be missed — anomaly detection covers that gap, not forecasting. |
| Accuracy is bounded by your data | If maintenance is logged inconsistently, failure prediction inherits that noise. Data quality work is usually the largest part of the first engagement. |
| Rare events stay hard | An event occurring twice a year gives roughly two training examples a year. We are explicit about which forecasts are strong and which are indicative. |
| A forecast is not a decision | The model produces probability and lead time. Whether to spend overtime to avoid a 60% risk is an operational judgement, and stays with a person. |
Predictive Analytics moves operations from reactive to predictive — with continuously retrained models grounded in your operational ground truth.
A 60-minute architecture review with our solutions team. We identify the forecasts that move your numbers — and the data we need to train them.